import requests
import asyncio
import aiohttp
import pandas as pd
import urllib
import gradio as gr
import os
from pymongo import MongoClient

addresses = [
    "0x46dae11e3ab74C417d370FB0d467364397178f5d",
    "0x2B3cb7D697Dd5224f3AcfC1b7EbDe77F468Dd539",
    "0x7B04395AF49caf8C4Fd5FF716698D97f394d0d41",
    "0x4dAFdbdB45947AB8666411AFc07F95b39Ab3841B",
    "0xa2ebb53A922E8fe9E6949316b0018F95f0956Ce7",
    "0x0FCc5f7845F3B2188918495f60F6294a15dc772b",
    "0xefa59c4aE5BaBA2c1017Ce2Fc9c1E0E080e5F448",
    "0x1C2A2D1b1A95a252AD4f9E8c9055885a1721c252",
    "0xfCb81D6a2678D9f0798124BD3E46bA0E1Ff0C32c",
    "0xdF04384C3AE43bD08E04692139C6B6C6Eb308B76",
    "0xA734C747E53171261568DbBB1BCd699EDF01925c",
    "0xC6347a0924aAbBA5E655397fbD18cb702066304b",
    "0xc6300201821CB7026E0e73cC10dC9Ca5C623Ff15",
    "0x2777698A53410C344e98267dB672a432c77C6Ed9",
    "0xCCc07e973c04c607381b96eCBe3e426cc7c17B88",
    "0xE813553077d0bfdd9615465D8A974F63bbf33aD4",
    "0x0a045118cDb3fB0FDb50D2f91f04c56B28F5fD07",
    "0x0bC67eE9A36f5bc8e05282E538FDe119C372a222",
    "0x1ca7bCe8A7c4F62a3575279688D9E9ad337f6b3d",
    "0x940665c733acBcA198306F8881c1d268E2F92DFA",
    "0x6393613ca5a87Cc2BEe2eC3610EcAcD279FcBcD6",
    "0xAB851cA6F8e5329Dcf9B164d312f7d5C9D41eE01",
    "0xe2Db92758567D1D67A861F0645F850ddb6e216E8",
    "0x4a7F42021C71Be21D3c21717743449620Abb3D25",
    "0x2Bd99Ac1dCa390A543BD06F7712ece7B787752a5",
    "0x8c27aa11FF3562c807C5A40e0512bDC043B39Bb0",
    "0xB4e6dEa305a1A84FA0B2185F36de31bcE2c22005",
    "0xA89f812f26ef2f6D89c81371A89787240c63D524",
    "0xF7a54082215D4508CD674b270ECc37Bd281FD92d",
    "0xc419410F5B29cdd4343013210ac194B872cF5a4B"
]

# Chunk into groups of 3
def chunk_addresses(addresses, size):
    return [addresses[i:i + size] for i in range(0, len(addresses), size)]

API_URL = "https://api.g.alchemy.com/data/v1/yhe6L3PXmiENzS1sP9Fu4_T5E3l0QyeB/assets/tokens/by-address"

async def fetch_batch(session, batch):
    json_payload = {
        "addresses": [{"address": addr, "networks": ["arb-mainnet"]} for addr in batch],
        "withMetadata": True,
        "withPrices": True,
        "includeNativeTokens": True
    }
    async with session.post(API_URL, json=json_payload) as response:
        response = await response.json()
        return response['data']['tokens']

async def get_wallet_balances(addresses):
    async with aiohttp.ClientSession() as session:
        batches = chunk_addresses(addresses, 3)
        tasks = [fetch_batch(session, batch) for batch in batches]
        responses = await asyncio.gather(*tasks)
        first_parts = []
        rest_parts = []

        for res in responses:
            first_parts.extend(res[:3])
            rest_parts.extend(res[3:])

        responses = first_parts + rest_parts
        df = pd.DataFrame(responses)
        for i in range(len(addresses)):
            df.at[i,'tokenMetadata'] = {}
    
        df['tokenBalance'] = df['tokenBalance'].apply(lambda x : int(x,16))
        df['decimals'] = df['tokenMetadata'].apply(lambda x : x.get('decimals',0))
        df['tokenBalance'] = df['tokenBalance']/(10**df['decimals'])
        for i in range(len(addresses)):
            df.at[i, 'tokenBalance'] = df.at[i, 'tokenBalance'] / (10**18)

        df['symbol'] = df['tokenMetadata'].apply(lambda x : x.get('symbol',''))
        for i in range(len(addresses)):
            df.at[i,'symbol'] = 'ETH'
        
        df['currentPrice'] = df['tokenPrices'].apply(lambda x : x[0].get('value',None) if len(x) > 0 else None)
        df.drop(columns=['tokenPrices','tokenMetadata','network','tokenAddress'], inplace=True)
        df = df.dropna()
        df['tokenBalance'] = pd.to_numeric(df['tokenBalance'], errors='coerce')
        df = df[df['tokenBalance'] != 0]
        df['tokenBalance'] = pd.to_numeric(df['tokenBalance'], errors='coerce')
        df['currentPrice'] = pd.to_numeric(df['currentPrice'], errors='coerce')
        df['value'] = df['tokenBalance'] * df['currentPrice']
        df.drop(columns=['decimals'],inplace=True)

        df = df[df['symbol'].isin(['BLUP', 'ETH', 'USDT', 'USDC'])]
        df= df[['address','symbol','tokenBalance','value']]
        df = df.rename(columns={'tokenBalance':'amount','value':'usd_value'})

        df = df.sort_values(['address', 'symbol'])
        df['address'] = df['address'].mask(df['address'].duplicated()).fillna('')

        #df = df.groupby("symbol", as_index=False).sum()

        return df, df['usd_value'].sum()

async def get_lp_balances():
    client = MongoClient(os.getenv('MONGO_DB_URI'))
    db = client["debank_tokens"]
    token_collection = db["debank"]
    token_list = token_collection.find_one(sort=[("timestamp", -1)])
    del token_list["_id"]
    del token_list["timestamp"]
    
    url = "https://api.debank.com/user?id=0x275c7f2781db0b78228029f85735059c9bdd4f35"

    SCRAPE_DO_TOKEN = os.getenv('SCRAPE_DO_KEY')

    headers = token_list

    encoded_url = urllib.parse.quote(url)
    proxy_url = f"http://api.scrape.do/?token={SCRAPE_DO_TOKEN}&url={encoded_url}&forwardHeaders=True&super=True"
    response = requests.get(proxy_url, headers=headers)

    response = response.json()['data']['user']['stats']
    top_coins = response['top_coins']
    top_tokens = response['top_tokens']
    coins = pd.DataFrame(top_coins)
    tokens = pd.DataFrame(top_tokens)
    balances = pd.concat([coins, tokens], axis=0)
    balances.drop(columns=['id','logo_url','percent','price','chain_id'],inplace=True)
    balances = balances[['symbol','amount','usd_value']]

    return balances, balances['usd_value'].sum()

async def get_all_dfs():
    wallet_df, wallet_total = await get_wallet_balances(addresses)
    lp_df, lp_total = await get_lp_balances()
    final_df = pd.concat([wallet_df,lp_df],axis=0)
    final_df.drop(columns=['address'],inplace=True)
    final_df = final_df.groupby("symbol", as_index=False).sum()
    starting_df = pd.DataFrame([{"symbol":"ETH","amount":24,"usd_value":1613*24}])
    pnl = final_df['usd_value'].sum() - starting_df['usd_value'].sum()
    return wallet_df, wallet_total, lp_df, lp_total, final_df, final_df['usd_value'].sum(),starting_df,starting_df['usd_value'].sum(),pnl


with gr.Blocks() as demo:
    gr.Markdown("## BLUP Balances and PnL")

    df3_out = gr.Dataframe(label="Starting Balances")
    txt3_out = gr.Textbox(label="Total Value")

    df0_out = gr.Dataframe(label="Wallet Balances")
    txt0_out = gr.Textbox(label="Total Value")

    df1_out = gr.Dataframe(label="LP Balances")
    txt1_out = gr.Textbox(label="Total Value")

    df2_out = gr.Dataframe(label="Total Balances")
    txt2_out = gr.Textbox(label="Total Value")

    pnl_out = gr.Textbox(label="PNL (AUM Model)")

    # Load from MongoDB on app load (sync function)
    demo.load(
    fn=  get_all_dfs, 
    inputs = [],
    outputs=[df0_out, txt0_out, df1_out, txt1_out, df2_out, txt2_out,df3_out,txt3_out,pnl_out]
    )

demo.launch(debug=True, share=True)